Pneumonia caused by novel coronavirus is an acute respiratory infectious disease. Its rapid spread in a short period of time has brought great challenges for global public health. The use of deep learning and radiomics methods can effectively distinguish the subtypes of lung diseases, provide better clinical prognosis accuracy, and assist clinicians, enabling them to adjust the clinical management level in time. The main goal of this study is to verify the performance of deep learning and radiomics methods in the classification of COVID-19 lesions and reveal the image characteristics of COVID-19 lung disease. An MFPN neural network model was proposed to extract the depth features of lesions, and six machine-learning methods were used to compare the classification performance of deep features, key radiomics features and combined features for COVID-19 lung lesions. The results show that in the COVID-19 image classification task, the classification method combining radiomics and deep features can achieve good classification results and has certain clinical application value.

Background: Atrial fibrillation (AF) is a common arrhythmia disorder with a high recurrence rate after catheter ablation for pulmonary vein isolation (PVI). Improved preoperative evaluation strategies are needed to enhance prediction accuracy and optimize patient selection for ablation. Materials and Methods: This study included 311 AF patients who underwent catheter ablation for PVI, stratified into recurrence ( n = 160) and non-recurrence ( n = 151) groups based on postoperative outcome. The deep learning model SwinUNETR was used to segment the heart region and extract the radiomics features of different areas of the heart. A risk prediction model was built using six machine learning methods. Finally, an interpretable analysis method based on radiomics was provided to capture the relationship between the radiological features of different heart structures and the recurrence of AF. Results: A feature selection process was applied to determine the most predictive features, resulting in a set of 50 radiomics features and 33 clinical features. The average dice value of the deep learning heart segmentation model was 88.95%, and the area under the curve (AUC) value of the radiomics model for predicting the risk of AF recurrence after PVI was 0.74(95% confidence interval (CI) 0.54, 0.79). The AUC value of the fusion model integrating clinical laboratory indicators and radiomic features was 0.79(95% CI 0.69–0.84). According to the results of the interpretability analysis of the model, multiple radiomics features were determined to be significantly associated with AF recurrence. Conclusion: This study presents a non-invasive model for predicting post-PVI recurrence and quantifies the contribution of specific cardiac structures to AF recurrence risk.